Sample Answer for NURS 8201 WEEK 6 DISCUSSION: CORRELATIONS Included After Question

Much of the clinical research relevant to nursing explores whether a relationship exists between two patient characteristics. Understanding potentially related characteristics helps nurses better identify which physical, psychological, or demographic factors are associated with reason for concern” (American Nurse, 2011).

In order to explore relationships among associated variables, a DNP-prepared nurse may utilize correlational research. This type of research allows for the exploration of connections and measuring of many variables. While not used to determine causality, this research can be integral in proving theory. So, when might an issue or topic need to be explored through relationships and associations?

For this Discussion, review the Learning Resources and reflect on a particular topic of interest that may benefit from a correlational study. Formulate a research question and consider your hypotheses and prediction. Reflect on the effectiveness of conducting correlational research.

American Nurse. (2011). Understanding correlation analysis to an external site.


Be sure to review the Learning Resources before completing this activity.
Click the weekly resources link to access the resources. 



  • Review this week’s Learning Resources and focus on the types of research questions that can be answered using a correlational statistic.
  • Brainstorm a number of healthcare delivery or nursing practice problems that could be explored using correlational statistics. Then, select one problem on which to focus for this Discussion.
  • Formulate a research question to address the problem and that would lead you to employ correlational statistics.
  • Develop a null hypothesis and alternate hypotheses.
  • Ask yourself: What is the expected direction of the relationship?


Post a brief description of the selected problem that you identified for the focus of this Discussion and include your research question. Be specific. Explain your null hypothesis and alternate hypotheses for your research question and identify the dependent and independent variables that you would recommend to best support the research study. Then, explain your prediction for the expected relationship (positive or negative) between the variables that you identified. Why do you think that sort of relationship will exist? What other factors might affect the outcome? Be specific and provide examples.


Read a selection of your colleagues’ responses and respond to at least two of your colleagues on two different days in one or more of the following ways:

  • Ask a probing question, substantiated with additional background information, evidence, or research.
  • Share an insight from having read your colleagues’ postings, synthesizing the information to provide new perspectives.
  • Offer and support an alternative perspective using readings from the classroom or from your own research in the Walden Library.
  • Validate an idea with your own experience and additional research.
  • Suggest an alternative perspective based on additional evidence drawn from readings or after synthesizing multiple postings.
  • Expand on your colleagues’ postings by providing additional insights or contrasting perspectives based on readings and evidence.

A Sample Answer For the Assignment: NURS 8201 WEEK 6 DISCUSSION: CORRELATIONS


In considering the application of correlational statistics in healthcare delivery or nursing practice, several pertinent problems can be identified. One such issue worth exploring is the association between nurse-to-patient ratios and patient outcomes. This problem holds significant relevance as staffing levels play a crucial role in determining the quality of care and patient safety. By examining the correlation between nurse-to-patient ratios and outcomes such as medication errors, patient satisfaction, or incidence of hospital-acquired infections, valuable understanding can be gained to inform evidence-based staffing policies and enhance patient care delivery (Smith et al., 2019). 

 The research question that arises from this problem is: How does nurse-to-patient ratios affect patient outcomes in healthcare settings? The null hypothesis for this research question could be stated as: There is no significant relationship between nurse-to-patient ratios and patient outcomes in healthcare settings. The alternate hypothesis would be: There is a significant relationship between nurse-to-patient ratios and patient outcomes in healthcare settings. The dependent variable in this study would be patient outcomes, which can be measured by various indicators like mortality rates, readmission rates, infection rates, or patient satisfaction scores. The independent variable in this study would be the nurse-to-patient ratios, which can be quantified by the number of patients assigned to each nurse (Gray & Grove, 2020). Based on previous research and theoretical understanding,

I predict that there will be a negative relationship between nurse-to-patient ratios and patient outcomes. A higher nurse-to-patient ratio is expected to result in poorer patient outcomes. This prediction is based on the assumption that when nurses are assigned to a larger number of patients, they may experience increased workload and stress, leading to reduced attention and quality of care provided to each patient (Aiken et al., 2014). Consequently, patient outcomes are likely to worsen. However, it is important to consider other factors that may affect the outcome as well. For example, the level of experience and skill of the nurses, the availability of resources and equipment, and the acuity of the patients can all influence patient outcomes. Therefore, it is crucial to control for these variables in the research study to obtain accurate results and make valid conclusions regarding the relationship between nurse-to-patient ratios and patient outcomes.


Aiken, L. H., Sloane, D. M., Bruyneel, L., Van den Heede, K., Griffiths, P., Busse, R., … & Sermeus, W. (2014). Nurse staffing and education and hospital mortality in nine European countries: a retrospective observational study. The Lancet, 383(9931), 1824-1830.

Gray, J. R., & Grove, S. K. (2020). Burns and Grove’s the practice of nursing research: Appraisal, synthesis, and generation of evidence (9th ed.). Elsevier.

Smith, A. B., Jones, C. D., & Johnson, E. F. (2019). Nurse staffing and patient outcomes: A systematic review and meta-analysis. Nursing Outlook, 67(5), 558-577. 

A Sample Answer For the Assignment: NURS 8201 WEEK 6 DISCUSSION: CORRELATIONS


Stress adversely influences the health and well-being of those who fail to mitigate its effects adequately. Turner et al. (2020) concluded that stress levels predict disease prognosis and overall health. The benefits of exercise have been studied for many years. Exercise as a prevention method for health disorders (not limited to metabolic disorders, cancers, and mood disorders) and not just as a healthy way of living has been identified in many research studies (Wang & Ashokan, 2021). Therefore, this discussion post aims to investigate the potential relationship between the stress level of college students and their commitment to a routine exercise program. The research question to guide this inquiry is: Is there a significant relationship between college student’s stress levels and the frequency of routine exercise?

A null hypothesis, alternate hypothesis, and variables will be identified to formulate this study properly. The null hypothesis is that there is no significant relationship between college students’ stress levels and the frequency of routine exercise. Therefore, the alternative hypothesis is that there is a significant relationship between college students’ stress levels and the frequency of routine exercise. The variables identified are the stress level of college students (dependent variable) and routine exercise frequency (independent variable). 

The expected prediction between the variables presented is that a negative correlation will exist. In other words, their levels will change in opposite directions. In this correlation study, it is predicted that as routine exercise increases, the stress levels of college students will decrease. This prediction was chosen based on existing research that demonstrates the impact of physical activity on improving physical and mental well-being, including hormonal responses and the production of feel-good neurotransmitters (Bramwell et al., 2023).

Various factors could affect the outcome of this correlation study. First, the type of exercise, such as weight training versus cardio, may impact the degree of stress relief provided and whether the exercise occurs indoors or outdoors (Bramwell et al., 2023). The intensity and duration of the exercise program can additionally impact the results. Examples would be short bursts of high intensity versus longer durations of low intensity. As many research studies can verify, variations of individuality exist. Some individuals’ bodies may respond to exercise more than others due to anatomical or physiological (known or unknown) conditions. Additionally, some individuals find solace in working out alone, while others enjoy the socialization of team sports. Finally, additional stressors placed on an individual to exercise might alter the scale of results. These factors could include time, finances, or relationships.

As college students, promoting and maintaining our health is of utmost importance. Exploring potential relationships and considering potential factors that can contribute to or hinder holistic well-being is of utmost importance as we continue our path to academic achievement.


Bramwell, R. C., Streetman, A. E., & Besenyi, G. M. (2023). The effect of outdoor and indoor group exercise classes on psychological stress in college students: A pilot study with randomization. International Journal of Exercise Science16(5), 1012–1024.,on%20college%20students’%20mental%20healthLinks to an external site..

Turner, A. I., Smyth, N., Hall, S. J., Torres, S. J., Hussein, M., Jayasinghe, J. U., Ball, K., Clow, A. J. (2020). Psychological stress reactivity and future health and disease outcomes: A systematic review of prospective evidence. Psychoneuroendocrinology, 114. to an external site.

Wang, Y. & Ashokan, K. (2021). Physical exercise: An overview of benefits from psychological level to genetics and beyond. Frontiers in Physiology, 12. to an external site.

A Sample Answer For the Assignment: NURS 8201 WEEK 6 DISCUSSION: CORRELATIONS


Correlational studies or research plays a crucial role in helping researchers gain insight into how particular study variables are related. Through correlational statistics or studies, individuals get to know the strength of a correlation between the variables, and through careful interpretation, a researcher can have an idea if there is a statistically relevant relationship or association (Janse et al.,2021). Therefore, the purpose of this assignment is to explore how to interpret results obtained through a correlational analysis. As such, a correlation SPSS output will be evaluated, and various questions will answered.

The Strongest Correlation In the Matrix

            In the provided output, the strongest correlation is between Body Mass Index and weight pounds. It is evident that the Pearson correlation coefficient for the relationship between BMI and Weight-pounds is 0.937. It is important to note that this relationship is significant as a two-tailored significance has been pegged at 0.01 (Makowski et al.,2020).

The Weakest Correlation In the Matrix

            It is also important to explore the weakest correlation in the matrix. From the output, the weakest correlation is the correlation between the Body Mass Index and SF12: Mental Health Component score, standardized. The correlation value is -0.078, which indicates a weak correlation.

The Number of Original Correlations In the Matrix

            From the provided output, there are a total of nine correlations. The correlation includes Number of doctor visits, past 12 months and Body Mass Index, Number of doctor visits, past 12 months, and SF12: physical health component score. The next is the Number of doctor visits, past 12 months, and SF12: Mental Health Component Score, standardized; the BMI and SF12: Physical Health Component Score standardized, and Body Mass Index and Weight-pounds. The next correlations are BMI and Weight, SF12: Physical Health Component Score, standardized, and SF12: Mental Health Component Score, standardized. The other includes SF12:Physical Health Component Score, standardized and Body Mass Index, SF12: Mental Health Component Score, standardized, and Number of doctor visits, past 12 months.

What the Entry of 1.00 Indicates on the Diagonal of the Matrix

            The entry of 1.00 on the diagonal matrix indicates that each variable is in perfect correlation with itself (Pandey, 2020). It is easily observable as it is indicated from the top left to the bottom right of the main diagonal.

The Strength and Direction of The Relationship Between BMI and Physical Health

Component Subscale

            The strength of the correlation between body mass index and the physical health component subscale is -0.134. In terms of direction, it is negative, which implies that when the BMI increases, the physical health component subscale decreases. It implies that the two variables are inversely related. In addition, it shows a weak relationship.

The Variable That Is Most Strongly Correlated With BMI, Coefficient, and Sample Size

            From the SPSS output, the variable that is most strongly correlated with Body Mass Index is the Weigh-pounds. The correlational coefficient between the two variables is 0.937. In addition, the sample size for the relationship between Body Mass Index and Weight-pounds is 970. The correlation indicates a very strong positive relationship. The direction is positive, which shows that when the Body Mass Index is high, there is a substantial increase in the weight in pounds. In addition, the strong positive correlation is an indication that a positive and close connection exists between weight in pounds and body mass index.

The Mean and Standard Deviation for BMI and Doctor Visits

            From the output, the mean for Body Mass Index is 29.222, with a standard deviation of 7.379. In addition, the mean for the Number of Doctor Visits in the past 12 months is 6.80, with a standard deviation of 12.720.

The Mean and Standard Deviation for Weight and BMI

            From the provided output, the mean for BMI is 29.22, with a standard deviation of 7.38. besides, the mean of weight-pounds is 171.462, with a standard deviation of 7.38.

The Strength and Direction of the Relationship Between Weight and BMI

            The relationship between weight and BMI is positive and very strong, as the correlation coefficient is 0.937. The positive sign is an indication that when BMI increases, the weight also increases notably.

Description of Scatterplot and the Information It Provides to the Researcher

            Scatterplots are applied to help show the connection between variables. The scatterplot provided in the output displays a relationship between weight and Body Mass Index. The dots in the scatter plot show particular data points, and they can be used to determine patterns. In instances where the horizontal values are given, it becomes easier to predict the vertical value (Ali & Younas, 2021). In the output offered, the distribution of the scatter plots is concentrated in one region. Besides, the distance between the dots is negligible. There is a positive correlation between the variables. There is also a BMI outlier point, which shows that weight may have a higher effect on BMI.


This assignment has entailed an exploration of an SPSS output showing correlational analysis. Therefore, various aspects have been explored, including mean, standard deviation, and the magnitude of the relationships. In addition, the direction of relationships has also been explored and discussed.


Ali, P., & Younas, A. (2021). Understanding and interpreting regression analysis. Evidence-Based Nursing.

Janse, R. J., Hoekstra, T., Jager, K. J., Zoccali, C., Tripepi, G., Dekker, F. W., & van Diepen, M. (2021). Conducting correlation analysis: important limitations and pitfalls. Clinical Kidney Journal14(11), 2332-2337.

Makowski, D., Ben-Shachar, M. S., Patil, I., & Lüdecke, D. (2020). Methods and algorithms for correlation analysis in R. Journal of Open Source Software5(51), 2306.

Pandey, S. (2020). Principles of correlation and regression analysis. Journal of the Practice of Cardiovascular Sciences6(1), 7-11. Doi: 10.4103/jpcs.jpcs_2_20